python-3.x - 为什么推导 db 变为负数？

Question

我正在从头开始构建猫和非猫深度学习模型。我已经初始化了参数 w 和 b。现在我被困在方法传播中。

我计算出相对于 b (db) 的损失梯度为 -0.0023151612773，但预期输出为 0.00145557813678。

此外 dw 为 [[ 0.99751791][ 2.39277178]] 但预期为 [[ 0.99845601] [ 2.39507239]]。

成本为 5.80237782517，但预期为 5.801545319394553。如果我犯了任何错误，请检查我的代码。谢谢

def propagate(w, b, X, Y):
    """
    Implement the cost function and its gradient for the propagation explained above

    Arguments:
    w -- weights, a numpy array of size (num_px * num_px * 3, 1)
    b -- bias, a scalar
    X -- data of size (num_px * num_px * 3, number of examples)
    Y -- true "label" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples)

    Return:
    cost -- negative log-likelihood cost for logistic regression
    dw -- gradient of the loss with respect to w, thus same shape as w
    db -- gradient of the loss with respect to b, thus same shape as b

    Tips:
    - Write your code step by step for the propagation. np.log(), np.dot()
    """

    m = X.shape[1]


    # FORWARD PROPAGATION (FROM X TO COST)
    ### START CODE HERE ### (≈ 2 lines of code)

    A = 1/(1+np.exp(-np.dot(w.T,X)+b))     # compute activation
    cost = -1/m*np.sum(np.dot(Y,np.log(A).T)+np.dot((1-Y),np.log(1-A).T))


    # compute cost
    ### END CODE HERE ###

    # BACKWARD PROPAGATION (TO FIND GRAD)
    ### START CODE HERE ### (≈ 2 lines of code)
    dw = 1/m*np.dot(X,(A-Y).T)
    db = 1/m*np.sum(A-Y)
    ### END CODE HERE ###

    assert(dw.shape == w.shape)
   # assert(db.dtype == float)
    cost = np.squeeze(cost)
    assert(cost.shape == ())

    grads = {"dw": dw,
             "db": db}
    #print (m)    
    return grads, cost

    w, b, X, Y = np.array([[1.],[2.]]), 2., np.array([[1.,2.,-1.],[3.,4.,-3.2]]), np.array([[1,0,1]])
    grads, cost = propagate(w, b, X, Y)
    print ("dw = " + str(grads["dw"]))
    print ("db = " + str(grads["db"]))
    print ("cost = " + str(cost))

标签： python-3.xneural-networkdeep-learning